SAC-Based Computation Offloading for Reconfigurable Intelligent Surface-Aided Mobile Edge Networks

In this paper,we concentrate on a reconfigurable intelligent surface(RIS)-aided mobile edge computing(MEC)system to improve the offload efficiency with moving user equipments(UEs).We aim to minimize the energy consumption of all UEs by jointly optimizing the discrete phase shift of RIS,UEs’transmitting power,computing resources allocation,and the UEs’task offloading strategies for local computing and offloading.The formulated problem is a sequential decision making across multiple coherent time slots.Furthermore,the mobility of UEs brings uncertainties into the decision-making process.To cope with this challenging problem,the deep reinforcement learning-based Soft Actor-Critic(SAC)algorithm is first proposed to effectively optimize the discrete phase of RIS and the UEs’task offloading strategies.Then,the transmitting power and computing resource allocation can be determined based on the action.Numerical results demonstrate that the proposed algorithm can be trained more stably and perform approximately 14%lower than the deep deterministic policy gradient benchmark in terms of energy consumption.

Security and Privacy in 5G Internet of Vehicles(IoV)Environment

Modern vehicles are equipped with sensors,communication,and computation units that make them capable of providing monitoring services and analysis of real-time traffic information to improve road safety.The main aim of communication in vehicular networks is to achieve an autonomous driving environment that is accident-free alongside increasing road use quality.However,the demanding specifications such as high data rate,low latency,and high reliability in vehicular networks make 5G an emerging solution for addressing the current vehicular network challenges.In the 5G IoV environment,various technologies and models are deployed,making the environment open to attacks such as Sybil,Denial of Service(DoS)and jamming.This paper presents the security and privacy challenges in an IoV 5G environment.Different categories of vehicular network attacks and possible solutions are presented from the technical point of view.

Column adsorption of Cu(Ⅱ) by polymer-supported nano-iron oxides in the presence of sulfate: Experimental and mathematical modeling

Polymer-supported hydrous iron oxides(HFOs) are promising for heavy metals removal from aqueous systems.The ubiquitous inorganic ligands, e.g., sulfate, are expected to exert considerable impacts on pollutants removal by these hybrid sorbents. Herein, we obtained a hybrid sorbent HFO-PS by encapsulating nanosized HFO into macroporous polystyrene(PS) resin. Both batch and column sorption experiments of Cu(Ⅱ) by HFO-PS were carried out in the presence of sulfate. Obviously, the presence of sulfate is favorable for Cu(Ⅱ) sorption onto HFO-PS.The performances of column Cu(Ⅱ) removal were fitted and predicted with Adams–Bohart, Clark, Thomas and BDST models. Thomas model is suggested best-fit to predict the breakthrough curves. Besides, a linear correlation is observed between breakthrough time and column length based on BDST model, which might be useful for predicting the breakthrough time for Cu(Ⅱ) removal by HFO-PS.

Analysis of the Efficiency-Energy with Regression and Classification in Household Using K-NN

This paper aims to study energy consumption in a house. Home energy managementsystem (HEMS) has become very important, because energy consumption of aresidential sector accounts for a significant amount of total energy consumption.However, a conventional HEMS has some architectural limitations among dimensionalvariables reusability and interoperability. Furthermore, the cost of implementation inHEMS is very expensive, which leads to the disturbance of the spread of a HEMS.Therefore, this study proposes an Internet of Things (IoT) based HEMS with lightweightphotovoltaic (PV) system over dynamic home area networks (DHANs), which enablesthe construction of a HEMS to be scalable reusable and interoperable. The study suggestsa technique for decreasing cost of energy that HEMS is using and various perspectives insystem. The method that proposed is K-NN (K-Nearest Neighbor) which helps us toanalyze the classification and regression datasets. This paper has the result from the datarelevant in October 2018 from some buildings of Nanjing University of InformationScience and Technology.

Fast Mesh Reconstruction from Single View Based on GCN and Topology Modification

3D reconstruction based on single view aims to reconstruct the entire 3D shape of an object from one perspective.When existing methods reconstruct the mesh surface of complex objects,the surface details are difficult to predict and the reconstruction visual effect is poor because the mesh representation is not easily integrated into the deep learning framework;the 3D topology is easily limited by predefined templates and inflexible,and unnecessary mesh self-intersections and connections will be generated when reconstructing complex topology,thus destroying the surface details;the training of the reconstruction network is limited by the large amount of information attached to the mesh vertices,and the training time of the reconstructed network is too long.In this paper,we propose a method for fast mesh reconstruction from single view based on Graph Convolutional Network(GCN)and topology modification.We use GCN to ensure the generation of high-quality mesh surfaces and use topology modification to improve the flexibility of the topology.Meanwhile,a feature fusion method is proposed to make full use of the features of each stage of the image hierarchically.We use 3D open dataset ShapeNet to train our network and add a new weight parameter to speed up the training process.Extensive experiments demonstrate that our method can not only reconstruct object meshes on complex topological surfaces,but also has better qualitative and quantitative results.

A Survey of Lower Limb Rehabilitation Systems and Algorithms Based on Functional Electrical Stimulation

Functional electrical stimulation is a method of repairing a dysfunctional limb in a stroke patient by using low-intensity electrical stimulation.Currently,it is widely used in smart medical treatment for limb rehabilitation in stroke patients.In this paper,the development of FES systems is sorted out and analyzed in a time order.Then,the progress of functional electrical stimulation in the field of rehabilitation is reviewed in details in two aspects,i.e.,system development and algorithm progress.In the system aspect,the development of the first FES control and stimulation system,the core of the lower limb-based neuroprosthesis system and the system based on brain-computer interface are introduced.The algorithm optimization for control strategy is introduced in the algorithm.Asynchronous stimulation to prolong the function time of the lower limbs and a method to improve the robustness of knee joint modeling using neural networks.Representative applications in each of these aspects have been investigated and analyzed.

Code-Based Preservation Mechanism of Electronic Record in Electronic Record Center of Cloud Storage

With the rapid development of E-commerce and E-government,there are so many electronic records have been produced.The increasing number of electronic records brings about storage difficulties,the traditional electronic records center is difficult to cope with the current fast growth requirements of electronic records storage and management.Therefore,it is imperative to use cloud storage technology to build electronic record centers.However,electronic records also have weaknesses in the cloud storage environment,and one of them is that once electronic record owners or managers lose physical control of them,the electronic records are more likely to be tampered with and destroyed.So,the paper builds a reliable electronic records preservation system based on coding theory.It can effectively guarantee the reliability of record storage when the electronic record is damaged,and the original electronic record can be restored by redundant coding,thus ensuring the reliable storage of electronic records.

Ensuring Readability of Electronic Records Based on Virtualization Technology in Cloud Storage

With the rapid development of E-commerce and E-government, there are somany electronic records have been produced. The increasing number of electronicrecords brings about storage difficulties, the traditional electronic records center isdifficult to cope with the current fast growth requirements of electronic records storageand management. Therefore, it is imperative to use cloud storage technology to buildelectronic record centers. However, electronic records also have weaknesses in the cloudstorage environment, and one of them is that once electronic record owners or managerslose physical control of them, the electronic records are more likely to be tampered withand destroyed. So, the paper builds a reliable electronic records preservation systembased on coding theory. It can effectively guarantee the reliability of record storage whenthe electronic record is damaged, and the original electronic record can be restored byredundant coding, thus ensuring the reliable storage of electronic records.

Intrusion Detection Method Based on Active Incremental Learning in Industrial Internet of Things Environment

Intrusion detection is a hot field in the direction of network security.Classical intrusion detection systems are usually based on supervised machine learning models.These offline-trained models usually have better performance in the initial stages of system construction.However,due to the diversity and rapid development of intrusion techniques,the trained models are often difficult to detect new attacks.In addition,very little noisy data in the training process often has a considerable impact on the performance of the intrusion detection system.This paper proposes an intrusion detection system based on active incremental learning with the adaptive capability to solve these problems.IDS consists of two modules,namely the improved incremental stacking ensemble learning detection method called Multi-Stacking model and the active learning query module.The stacking model can cope well with concept drift due to the diversity and generalization selection of its base classifiers,but the accuracy does not meet the requirements.The Multi-Stacking model improves the accuracy of the model by adding a voting layer on the basis of the original stacking.The active learning query module improves the detection of known attacks through the committee algorithm,and the improved KNN algorithm can better help detect unknown attacks.We have tested the latest industrial IoT dataset with satisfactory results.

LKAW: A Robust Watermarking Method Based on Large Kernel Convolution and Adaptive Weight Assignment

Robust watermarking requires finding invariant features under multiple attacks to ensure correct extraction.Deep learning has extremely powerful in extracting features,and watermarking algorithms based on deep learning have attracted widespread attention.Most existing methods use 3×3 small kernel convolution to extract image features and embed the watermarking.However,the effective perception fields for small kernel convolution are extremely confined,so the pixels that each watermarking can affect are restricted,thus limiting the performance of the watermarking.To address these problems,we propose a watermarking network based on large kernel convolution and adaptive weight assignment for loss functions.It uses large-kernel depth-wise convolution to extract features for learning large-scale image information and subsequently projects the watermarking into a highdimensional space by 1×1 convolution to achieve adaptability in the channel dimension.Subsequently,the modification of the embedded watermarking on the cover image is extended to more pixels.Because the magnitude and convergence rates of each loss function are different,an adaptive loss weight assignment strategy is proposed to make theweights participate in the network training together and adjust theweight dynamically.Further,a high-frequency wavelet loss is proposed,by which the watermarking is restricted to only the low-frequency wavelet sub-bands,thereby enhancing the robustness of watermarking against image compression.The experimental results show that the peak signal-to-noise ratio(PSNR)of the encoded image reaches 40.12,the structural similarity(SSIM)reaches 0.9721,and the watermarking has good robustness against various types of noise.

Multi-Path Attention Inverse Discrimination Network for Offline Signature Verification

Signature verification,which is a method to distinguish the authenticity of signature images,is a biometric verification technique that can effectively reduce the risk of forged signatures in financial,legal,and other business envir-onments.However,compared with ordinary images,signature images have the following characteristics:First,the strokes are slim,i.e.,there is less effective information.Second,the signature changes slightly with the time,place,and mood of the signer,i.e.,it has high intraclass differences.These challenges lead to the low accuracy of the existing methods based on convolutional neural net-works(CNN).This study proposes an end-to-end multi-path attention inverse dis-crimination network that focuses on the signature stroke parts to extract features by reversing the foreground and background of signature images,which effectively solves the problem of little effective information.To solve the problem of high intraclass variability of signature images,we add multi-path attention modules between discriminative streams and inverse streams to enhance the discriminative features of signature images.Moreover,a multi-path discrimination loss function is proposed,which does not require the feature representation of the samples with the same class label to be infinitely close,as long as the gap between inter-class distance and the intra-class distance is bigger than the set classification threshold,which radically resolves the problem of high intra-class difference of signature images.In addition,this loss can also spur the network to explore the detailed infor-mation on the stroke parts,such as the crossing,thickness,and connection of strokes.We respectively tested on CEDAR,BHSig-Bengali,BHSig-Hindi,and GPDS Synthetic datasets with accuracies of 100%,96.24%,93.86%,and 83.72%,which are more accurate than existing signature verification methods.This is more helpful to the task of signature authentication in justice and finance.

Person Re-Identification with Model-Contrastive Federated Learning in Edge-Cloud Environment

Person re-identification(ReID)aims to recognize the same person in multiple images from different camera views.Training person ReID models are time-consuming and resource-intensive;thus,cloud computing is an appropriate model training solution.However,the required massive personal data for training contain private information with a significant risk of data leakage in cloud environments,leading to significant communication overheads.This paper proposes a federated person ReID method with model-contrastive learning(MOON)in an edge-cloud environment,named FRM.Specifically,based on federated partial averaging,MOON warmup is added to correct the local training of individual edge servers and improve the model’s effectiveness by calculating and back-propagating a model-contrastive loss,which represents the similarity between local and global models.In addition,we propose a lightweight person ReID network,named multi-branch combined depth space network(MB-CDNet),to reduce the computing resource usage of the edge device when training and testing the person ReID model.MB-CDNet is a multi-branch version of combined depth space network(CDNet).We add a part branch and a global branch on the basis of CDNet and introduce an attention pyramid to improve the performance of the model.The experimental results on open-access person ReID datasets demonstrate that FRM achieves better performance than existing baseline.

Molecular-level proton acceptor boosts oxygen evolution catalysis to enable efficient industrial-scale water splitting

Industrial water splitting has long been suppressed by the sluggish kinetics of the oxygen evolution reaction(OER),which requires a catalyst to be efficient.Herein,we propose a molecular-level proton acceptor strategy to produce an efficient OER catalyst that can boost industrial-scale water splitting.Molecular-level phosphate(-PO_(4))group is introduced to modify the surface of PrBa_(0.5)Ca_(0.5)Co_(2)O_(5)+δ(PBCC).The achieved catalyst(PO_(4)-PBCC)exhibits significantly enhanced catalytic performance in alkaline media.Based on the X-ray absorption spectroscopy results and density functional theory(DFT)calculations,the PO_(4)on the surface,which is regarded as the Lewis base,is the key factor to overcome the kinetic limitation of the proton transfer process during the OER.The use of the catalyst in a membrane electrode assembly(MEA)is further evaluated for industrial-scale water splitting,and it only needs a low voltage of 1.66 V to achieve a large current density of 1 A cm^(-2).This work provides a new molecular-level strategy to develop highly efficient OER electrocatalysts for industrial applications.

A Non-Parametric Scheme for Identifying Data Characteristic Based on Curve Similarity Matching

For accurately identifying the distribution charac-teristic of Gaussian-like noises in unmanned aerial vehicle(UAV)state estimation,this paper proposes a non-parametric scheme based on curve similarity matching.In the framework of the pro-posed scheme,a Parzen window(kernel density estimation,KDE)method on sliding window technology is applied for roughly esti-mating the sample probability density,a precise data probability density function(PDF)model is constructed with the least square method on K-fold cross validation,and the testing result based on evaluation method is obtained based on some data characteristic analyses of curve shape,abruptness and symmetry.Some com-parison simulations with classical methods and UAV flight exper-iment shows that the proposed scheme has higher recognition accuracy than classical methods for some kinds of Gaussian-like data,which provides better reference for the design of Kalman filter(KF)in complex water environment.

The torsion model of soft tissue in virtual surgery

In order to solve the problem of real-time soft tissue torsion simulation in virtual surgeries,a torsion model based on coil spring is proposed to actualize real-time interactions and applications in virtual surgeries. The proposed model is composed of several connected coil springs in series. The sum of torsion deformation on every coil is equivalent to the soft tissue surface deformation. The calculation of the model is simple because the method for calculating the torsion deformation for each coil spring is the same. The virtual surgery simulation system is established on PHANTOM OMNI haptic device based on the Open GL 3 D graphic interface and VC + + software,and it is used to simulate the torsion deformation of virtual legs and arms. Experimental results show that the proposed model can effectively simulate the torsion deformation of soft tissue while being of real-time performance and simplicity,which can well meet requirements of virtual operation simulations.

Recent advances in bismuth-based multimetal oxide photocatalysts for hydrogen production from water splitting:Competitiveness,challenges,and future perspectives

The efficient utilization of photocatalytic technology is essential for clean energy.Bismuth-based multimetal oxides(Bi_(2)WO_(6),Bi_(2)MoO_(6),BiVO_(4)and Bi_(4)Ti_(3)O_(12))have aroused widespread attention as a visible light responsive photocatalyst for hydrogen evolution due to their low cost,nontoxicity,modifiable morphology,and outstanding optical and chemical properties.Nevertheless,the photocatalytic activities of pure materials are unsatisfactory because of their relative small specific surface area,poor quantum yield,and the rapid recombination of photogenerated carriers.Therefore,some modification strategies,including morphological control,semiconductor combination,doping,and defect engineering,have been systematically studied to enhance photocatalytic H_(2)evolution activity in the past few years.Herein,we summarize the recent research progress on bismuth-based photocatalysts,pointing out the prospects,opportunities and challenges of bismuth-based photocatalysts.Eventually,we aims to put forward valuable suggestions for designing of bismuth-based photocatalysts applied in hydrogen production on the premise of consolidating the existing theoretical basis of photocatalysis.

A memristive map with coexisting chaos and hyperchaos

By introducing a discrete memristor and periodic sinusoidal functions,a two-dimensional map with coexisting chaos and hyperchaos is constructed.Various coexisting chaotic and hyperchaotic attractors under different Lyapunov exponents are firstly found in this discrete map,along with which other regimes of coexistence such as coexisting chaos,quasiperiodic oscillation,and discrete periodic points are also captured.The hyperchaotic attractors can be flexibly controlled to be unipolar or bipolar by newly embedded constants meanwhile the amplitude can also be controlled in combination with those coexisting attractors.Based on the nonlinear auto-regressive model with exogenous inputs(NARX)for neural network,the dynamics of the memristive map is well predicted,which provides a potential passage in artificial intelligencebased applications.

Embedding any desired number of coexisting attractors in memristive system

A simple variable-boostable system is selected as the structure for hosting an arbitrarily defined memristor for chaos producing.The derived three-dimensional(3-D)memristive chaotic system shows its distinct property of offset,amplitude and frequency control.Owing its merits any desired number of coexisting attractors are embedded by means of attractor doubling and self-reproducing based on function-oriented offset boosting.In this circumstance two classes of control gates are found:one determines the number of coexisting attractors resorting to the independent offset controller while the other is the initial condition selecting any one of them.Circuit simulation gives a consistent output with theoretically predicted embedded attractors.

A Novel Twist Deformation Model of Soft Tissue in Surgery Simulation

Real-time performance and accuracy are two most challenging requirements in virtual surgery training.These difficulties limit the promotion of advanced models in virtual surgery,including many geometric and physical models.This paper proposes a physical model of virtual soft tissue,which is a twist model based on the Kriging interpolation and membrane analogy.The proposed model can quickly locate spatial position through Kriging interpolation method and accurately compute the force change on the soft tissue through membrane analogy method.The virtual surgery simulation system is built with a PHANTOM OMNI haptic interaction device to simulate the torsion of virtual stomach and arm,and further verifies the real-time performance and simulation accuracy of the proposed model.The experimental results show that the proposed soft tissue model has high speed and accuracy,realistic deformation,and reliable haptic feedback.

A local high-resolution deformation model of soft tissue based on element-free Galerkin method

In order to solve the problem that the existing meshless models are of high computational complexity and are difficult to express the biomechanical characteristics of real soft tissue, a local high-resolution deformation model of soft tissue based on element-free Galerkin method is proposed. The proposed model applies an element-free Galerkin method to establish the model, and integrates Kelvin viscoelastic model and adjustment function to simulate nonlinear viscoelasticity of soft tissue. Meanwhile, a local high-resolution algorithm is applied to sample and render the deformed region of the model to reduce the computational complexity. To verify the effectiveness of the model,liver and brain tumor deformation simulation experiments are carried out. The experimental results show that compared with the existing meshless models, the proposed model well reflects the biomechanical characteristics of soft tissue, and is of high authenticity, which can provide better visual feedback to users while reducing computational cost.